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Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge.

copied from cf-post-staging / r-grpreg
Type Size Name Uploaded Downloads Labels
conda 363.5 kB | win-64/r-grpreg-3.3.1-r41h6d2157b_0.tar.bz2  5 years and 2 days ago 833 main
conda 373.3 kB | osx-64/r-grpreg-3.3.1-r41h28b5c78_0.tar.bz2  5 years and 2 days ago 157 main
conda 369.1 kB | linux-64/r-grpreg-3.3.1-r41hcfec24a_0.tar.bz2  5 years and 2 days ago 3295 main
conda 380.5 kB | win-64/r-grpreg-3.3.1-r36h6d2157b_0.tar.bz2  5 years and 1 month ago 928 main
conda 379.4 kB | win-64/r-grpreg-3.3.1-r40h6d2157b_0.tar.bz2  5 years and 1 month ago 874 main
conda 372.6 kB | osx-64/r-grpreg-3.3.1-r40h28b5c78_0.tar.bz2  5 years and 1 month ago 250 main
conda 373.5 kB | osx-64/r-grpreg-3.3.1-r36h28b5c78_0.tar.bz2  5 years and 1 month ago 207 main
conda 369.5 kB | linux-64/r-grpreg-3.3.1-r40hcfec24a_0.tar.bz2  5 years and 1 month ago 3379 main
conda 370.3 kB | linux-64/r-grpreg-3.3.1-r36hcfec24a_0.tar.bz2  5 years and 1 month ago 3500 main

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